We implemented the three image encoding algorithms described in Sec.  as well as the variational quantum circuits using PennyLane ([1]}), a quantum machine learning and optimization framework with the ability to calculate gradients of variational quantum circuits. The classical parts of the model are implemented using PyTorch ([2]}).
Our implementation of QNNs follows the implementation of [3]} in the way that the measured expectation values of each qubit are written in a separate layer of the feature map. This is is different to [4]}, who post-process the expectation values by a decoding function that returns a scalar value rather than a vector.
